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In the modern era of computing, single-core processors are becoming obsolete, with devices like smartphones and laptops utilizing multiple cores to enhance performance. By applying parallel processing techniques, we can achieve higher throughput and lower power consumption. This involves strategies such as multi-threading and multi-processing, enabling applications to run concurrently. Notably, scaling voltage and frequency effectively can significantly diminish power usage while maintaining performance levels. Understanding Amdahl’s Law helps guide optimization efforts in parallel computing environments.
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Parallel Processors Todd CharltonEric Uriostique
Current Technology • Hard to find a single core processor anymore. • Cell phones, Labtops, etc. • Large systems can contain up to 512+processors
The Motivation • Divide and Conquer – Higher Throughput • Lower Power Consumption P = CV2f
The Motivation • We need more performance on same power budget. How? • Remember: P = CV2f • Scale voltage and frequency to80% • P = C * .82 [V] * .8 [f] • This drops power by 50% • Add additional core • Result = 1.6x Speedup with same power
The Motivation • How about reducing power consumption but keeping the same performance? • Remember: P = CV2f • Scale voltage and frequency by 50% • P = C * .52 [V] * .5 [f] • This drops power to 12.5% • Add additional core • Result = 25% of original power consumption with same performance
Amdahl’s Law • “Speed-up is limited by amount of work that can be done in parallel” Credit: watermint.org
Ways To Parallelize • Multi-Threading: • Multi-thread your application on one chip • More elegant • Multi-Processing: • Flash serial code to separate chips • No worrying about scheduling!
Let’s Multi-Thread • One Application: Counting maize pixels 2 Processors 4 Processors
Multi-Threading in µProcessors • Spin Propeller Processor • Multi-Thread on8 cores • One application run on 8 cores • Uses it’s own high level language and a form of Assembly • In CMU Cam4
Problems with Multi-Threading • Steep learning curve • Learning the Language • Parallel Slowdown • Lot of time to set up a new thread. If that thread does not have much work, not worth the overhead
Multi-Threading Libraries • Cannot program serially to take advantage of Parallel Processing • Intel’s Thread Building Blocks (TBB) • OpenMP • Boost and pthread • All of these are libraries in C/C++
Multi-Processing:Beaglebone • Processor • 720 MHz ARM Cortex-A8 • 3D graphics accelerator • ARM Cortex-M3 for power management • 2x Programmable Realtime Unit RISC CPUs • PRUs share memory space with A8
Multi-Processing:Custom with Message Passing • Designate a processor for each frequent tasks • Send messages to "Boss" as necessary • Since every processor's workload is minimal, slower and low power chips can be used • Overall = Same system performance
Problems with Multi-Processing • Shared Memory Space • Boards like this are hard to find and configure • Message Passing • Can’t assume messages are received immediately
Recap • Go parallel if you want: • Higher Throughput • Lower Power • Two Ways: • Multi-Threading – Spin • Speed up one Application • Multi-Processing – Beaglebone • Do more tasks at same time • Don’t forget Amdahl’s Law!
